Ensemble Classifiers for Medical Diagnosis of Knee Osteoarthritis Using Gait Data

Automated or semi-automated gait analysis systems are important in assisting physicians for diagnosis of various diseases. The objective of this study is to discuss ensemble methods for gait classification as a part of preliminary studies of designing a semi-automated diagnosis system. For this purpose gait data is collected from 110 sick subjects (having knee Osteoarthritis (OA)) and 91 age-matched normal subjects. A set of Multilayer Perceptrons (MLPs) is trained by using joint angle and time-distance parameters of gait as features. Large dimensional feature vector is decomposed into feature subsets and the ones selected by gait expert are used to categorize subjects into two classes; healthy and patient. Ensemble of MLPs is built using these distinct feature subsets and diversification of classifiers is analyzed by cross-validation approach and confusion matrices. High diversifications observed in the confusion matrices suggested that using combining methods would help. Indeed, when a proper combining rule is applied to decomposed sets, more accurate results are obtained. The result suggests that ensemble of MLPs could be applied in the automated diagnosis of gait disorders in a clinical context.